AlignCAPE: Support and Query Feature Aligning for Category-Agnostic Pose Estimation

Abstract

Recent advancements in category-agnostic pose estimation have focused on developing a unified model capable of localizing keypoint coordinates across arbitrary categories, which enables robots to accurately interact with diverse objects by understanding their poses. While existing methods predominantly concentrate on local features surrounding the keypoints of the support image, they often overlook the importance of global features, leading to potential misalignment between the support and query image. To address the inherent conflicts between the two images, we propose AlignCAPE, a novel approach designed to mitigate such misalignment and enhance the model performance. Our method formulates a two-stage pipeline, generating initial proposals in the first stage, followed by another stage to refine iteratively. Specifically, we introduce two modules, Feature Alignment Module(FAM) and Keypoint Perception Module(KPM). FAM utilizes bidirectional cross-attention operation to align the support image feature and query image feature, thereby compensating for the limitations of previous methods. KPM employs self-attention mechanism to capture the interactions among keypoints, facilitating to localize keypoints in the query image. Experiments on MP-100 benchmark demonstrate that our method outperforms the widely-used baseline model in CAPE by 0.68% in PCK@0.2 metric under 1-shot setting.